SSDA uses spectral magnitude alignment and structural-guided low-rank adaptation to close frequency and adjacency gaps when large vision models process time series rendered as images.
Sparse learned kernels for interpretable and efficient medical time series processing.Nature machine intelligence, 6(10):1132–1144
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SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecasting
SSDA uses spectral magnitude alignment and structural-guided low-rank adaptation to close frequency and adjacency gaps when large vision models process time series rendered as images.